def create_and_check_decoder_model_past_large_inputs(
self, config, inputs_dict):
model = PLBartModel(
config=config).get_decoder().to(torch_device).eval()
input_ids = inputs_dict["input_ids"]
attention_mask = inputs_dict["attention_mask"]
head_mask = inputs_dict["head_mask"]
# first forward pass
outputs = model(input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
use_cache=True)
output, past_key_values = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([attention_mask, next_attn_mask],
dim=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask)["last_hidden_state"]
output_with_past_key_values = model(next_tokens,
attention_mask=next_attention_mask,
past_key_values=past_key_values)
output_from_past = output_with_past_key_values["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1, ), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:,
random_slice_idx].detach(
)
output_from_past_slice = output_from_past[:, :,
random_slice_idx].detach()
self.parent.assertTrue(
output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(
torch.allclose(output_from_past_slice,
output_from_no_past_slice,
atol=1e-3))
def check_encoder_decoder_model_standalone(self, config, inputs_dict):
model = PLBartModel(config=config).to(torch_device).eval()
outputs = model(**inputs_dict)
encoder_last_hidden_state = outputs.encoder_last_hidden_state
last_hidden_state = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
encoder = model.get_encoder()
encoder.save_pretrained(tmpdirname)
encoder = PLBartEncoder.from_pretrained(tmpdirname).to(
torch_device)
encoder_last_hidden_state_2 = encoder(
inputs_dict["input_ids"],
attention_mask=inputs_dict["attention_mask"])[0]
self.parent.assertTrue(
(encoder_last_hidden_state_2 -
encoder_last_hidden_state).abs().max().item() < 1e-3)
with tempfile.TemporaryDirectory() as tmpdirname:
decoder = model.get_decoder()
decoder.save_pretrained(tmpdirname)
decoder = PLBartDecoder.from_pretrained(tmpdirname).to(
torch_device)
last_hidden_state_2 = decoder(
input_ids=inputs_dict["decoder_input_ids"],
attention_mask=inputs_dict["decoder_attention_mask"],
encoder_hidden_states=encoder_last_hidden_state,
encoder_attention_mask=inputs_dict["attention_mask"],
)[0]
self.parent.assertTrue((last_hidden_state_2 -
last_hidden_state).abs().max().item() < 1e-3)